Search Results (178)

Fossil fuels have been the main source of energy for decades. However, they are non-renewable resources that take millions of years to replenish from decomposed organic matter. As they are depleting at an alarming rate, a shift towards more sustainable fuels is gaining popularity. Biodiesel is emerging as a biodegradable and renewable energy source that serves as a promising alternative to conventional fuels. It addresses the challenges of greenhouse gas emissions while ensuring energy security. Among potential feedstocks, sunflower oil demonstrates unique advantages due to its high oil yield, favorable fatty acid composition, and availability. Despite extensive research on biodiesel, no comparative study has yet synthesized the four generations of biodiesel feedstocks while integrating optimization strategies with a particular focus on sunflower oil and sustainability trade-offs. This review aims to fill that gap by providing a comprehensive analysis of biodiesel production pathways, highlighting sunflower oil within a broader sustainability framework. The four generations are assessed based on feedstock potential, efficiency, and yield, while optimization processes for sunflower oil-based biodiesel are examined in terms of economic feasibility, limitations, and environmental impacts. The principal findings highlight the low free fatty acid composition of sunflower oil compared to other feedstocks, which makes it efficient for transesterification. Challenges such as production costs, land consumption, and food chain disruption are also discussed. Finally, innovative insights are presented for improving the viability of biodiesel through advanced technologies and supportive policies. Full article

Hydrogen is widely regarded as a promising clean energy carrier, and blending hydrogen into existing natural gas pipelines is considered a cost-effective and practical pathway for large-scale deployment. Supplying hydrogen-enriched natural gas to buildings requires careful consideration of the safe operation of pipelines and appliances without introducing new risks. In this study, on-site demonstrations and experimental tests were conducted in two high-rise buildings in Weifang to evaluate the impact of hydrogen addition on high-rise building natural gas distribution systems. The results indicate that hydrogen blending up to 20% by volume does not cause stratification in building risers and leads only to a relatively minor increase in additional pressure, approximately 0.56 Pa/m for every 10% increase in hydrogen addition. While hydrogen addition may increase leakage primarily in aging indoor gas systems, gas meter leakage rates under a 10% hydrogen blend remain below 3 mL/h, satisfying safety requirements. In addition, in-service domestic gas alarms remain effective under hydrogen ratios of 0–20%, with average response times of approximately 19–20 s. These findings help clarify the safety performance of hydrogen-blended natural gas in high-rise building distribution systems and provide practical adjustment measures to support future hydrogen injection projects. Full article

Landmines remain a significant hazard in contemporary warfare and post-conflict areas, jeopardizing the safety of both civilians and military personnel. This work suggests “Defence Pal,” a cost-effective and portable robotic prototype for landmine detection and environmental monitoring. Its primary objective is to minimize human risk while improving detection speed and accuracy. The system consists of a wireless-controlled vehicle equipped with a metal detector, gas sensors, and obstacle avoidance features, enabling real-time terrain surveillance while ensuring operator safety. Built with components including a Flysky FS-i6 transmitter and receiver, the prototype was tested under hazardous conditions. It demonstrated reliable detection of buried metallic objects and dangerous gases such as methane and carbon dioxide. The autonomous response system halts the robot and activates visual and auditory alarms upon detecting threats. Our experiments achieved average detection accuracies of 83% for metallic objects and 87% for hazardous gases, validating their performance. These results highlight the practicality and effectiveness of Defence Pal compared to conventional manual detection methods. The results confirm that Defence Pal is a practical, scalable, and cost-effective alternative to traditional manual detection methods for improving landmine identification and environmental hazard monitoring. Therefore, the novelty of this work lies in a low-cost dual-sensing prototype that enables simultaneous detection of gas and metal, providing a practical alternative to conventional single-target, high-cost systems. Full article

The blending of hydrogen significantly impacts the diffusion and safety characteristics of natural gas within indoor environments. This study employs ANSYS Fluent 2021 R1 to numerically investigate the diffusion and ventilation characteristics of hydrogen-blended natural gas (HBNG) leakage in indoor spaces. A physical and mathematical model of gas leakage from pipelines is established to study hazardous areas, flammable regions, ventilation characteristics, alarm response times, safe ventilation rates, and the concentration distribution of leaked gas. The effects of hydrogen blending ratio (HBR), ventilation conditions, and space dimensions on leakage diffusion and safety are analyzed. Results indicate that HBNG leakage forms vertical concentration stratification in indoor spaces, with ventilation height being negatively correlated with gas concentration and flammable regions. In the indoor space conditions of this study, by improving ventilation conditions, the hazardous area can be reduced by up to 92.67%. Increasing HBR substantially expands risk zones—with pure hydrogen producing risk volumes over five times greater than natural gas. Mechanical ventilation significantly enhances indoor safety. Safe ventilation rates escalate with hydrogen content, providing quantitative safety criteria for HBNG implementation. The results underscore the critical influence of HBR and ventilation strategy on risk assessment, providing essential insights for the safe indoor deployment of HBNG. Full article

Electric submersible pumps (ESPs) are among the most widely used artificial lifting systems, and their operational stability is crucial to the production capacity and lifespan of oil wells. However, during the operation of ESP systems, they often face complex flow issues such as gas lock and insufficient liquid carry. Traditional control strategies relying on liquid level monitoring and electrical parameter alarms exhibit obvious latency, making it difficult to effectively guide the adjustments of key operating parameters such as pump frequency, valve opening, and on/off strategies. To monitor the flow state of ESP systems and optimize it in a timely manner, this paper proposes an innovative profile recognition method based on multiphase flow fitting in the wellbore, aimed at reconstructing the flow state at the pump’s intake. This method identifies flow abnormalities and, in conjunction with flow characteristics, designs targeted operating parameter optimization logic to enhance the stability and efficiency of ESP systems. Research shows that this optimization method can significantly improve the pump’s operational performance, reduce failure rates, and extend equipment lifespan, thus providing an effective solution for optimizing production in electric pump wells. Additionally, this method holds significant importance for enhancing oil well production efficiency and economic benefits, providing a scientific theoretical foundation and practical guidance for future oil and gas exploration and management. Full article

This review critically evaluates the performance of approximately 80 commercially available mobile detectors for explosive identification. The majority of devices utilize Ion Mobility Spectrometry (IMS), Fourier Transform Infrared Spectroscopy (FTIR), or Raman Spectroscopy (RS). IMS-based instruments, such as the M-ION (Inward Detection), typically achieve sensitivities at the ppt level, while other IMS implementations demonstrate detection ranges from low ppb to ppm. Gas Chromatography–Mass Spectrometry (GC–MS) systems, represented by the Griffin™ G510 (Teledyne FLIR Detection), provide detection limits in the ppb range. Transportable Mass Spectrometers (Bay Spec) operate at ppb to ppt levels, whereas Laser-Induced Fluorescence (LIF) devices, such as the Fido X4 (Teledyne FLIR Detection), achieve detection at the nanogram level. Quartz Crystal Microbalance (QCM) sensors, exemplified by the EXPLOSCAN (MS Technologies Inc. 8609 Westwood Center Drive Suite 110, Tysons Corner, VA, USA), typically reach the ppb range. Only four devices employ two orthogonal analytical techniques, enhancing detection reliability and reducing false alarms. Traditional colorimetric tests based on reagent–analyte reactions remain in use, demonstrating the continued relevance of simple yet effective methods. By analyzing the capabilities, limitations, and technological trends of current detection systems, this study underscores the importance of multi-technique approaches to improve accuracy, efficiency, and operational effectiveness in real-world applications. The findings provide guidance for the development and selection of mobile detection technologies for security, defense, and emergency response. Full article

To reveal the dispersion characteristics of gas leaks in a comprehensive pipe gallery under different leakage parameters, a refined model for gas leak dispersion was established based on CFD simulation. By studying parameters such as alarm time, methane diffusion distance, and backflow length, the impact of leakage aperture and pipeline operating pressure on the distribution characteristics of gas leaks in the comprehensive pipe gallery was investigated. Furthermore, prediction models for alarm time, methane diffusion distance, and backflow length were developed. The results show the following: (a) When the pipeline operating pressure is constant, the leakage rate increases according to a power-law relationship with the size of the leakage aperture. However, when the leakage aperture size is constant, the leakage rate exhibits a linear relationship with the pipeline operating pressure; (b) The alarm time decreases with an increase in both the leakage aperture and pipeline operating pressure. Similarly, the methane diffusion distance increases with an increase in these two factors. Moreover, the methane backflow length increases according to a power-law relationship with the dimensionless leakage aperture and pipeline operating pressure, with exponents of 0.83 and 0.63, respectively. (c) The fitted predictive models for alarm time and methane diffusion distance yielded correlation coefficients of 0.97 and 0.98, with average residuals of 2.53 and 1.97, respectively, at each point. These findings can further provide a basis for the safe operation of the underground comprehensive pipe gallery. Full article

Pipelines play a vital role in material transportation within industrial settings. This review synthesizes detection technologies for early-stage small gas leaks from pipelines in the industrial sector, with a focus on acoustic-based methods, optical gas imaging (OGI), and multimodal fusion approaches. It encompasses detection principles, inherent challenges, mitigation strategies, and the state of the art (SOTA). Small leaks refer to low flow leakage originating from defects with apertures at millimeter or submillimeter scales, posing significant detection difficulties. Acoustic detection leverages the acoustic wave signals generated by gas leaks for non-contact monitoring, offering advantages such as rapid response and broad coverage. However, its susceptibility to environmental noise interference often triggers false alarms. This limitation can be mitigated through time-frequency analysis, multi-sensor fusion, and deep-learning algorithms—effectively enhancing leak signals, suppressing background noise, and thereby improving the system’s detection robustness and accuracy. OGI utilizes infrared imaging technology to visualize leakage gas and is applicable to the detection of various polar gases. Its primary limitations include low image resolution, low contrast, and interference from complex backgrounds. Mitigation techniques involve background subtraction, optical flow estimation, fully convolutional neural networks (FCNNs), and vision transformers (ViTs), which enhance image contrast and extract multi-scale features to boost detection precision. Multimodal fusion technology integrates data from diverse sensors, such as acoustic and optical devices. Key challenges lie in achieving spatiotemporal synchronization across multiple sensors and effectively fusing heterogeneous data streams. Current methodologies primarily utilize decision-level fusion and feature-level fusion techniques. Decision-level fusion offers high flexibility and ease of implementation but lacks inter-feature interaction; it is less effective than feature-level fusion when correlations exist between heterogeneous features. Feature-level fusion amalgamates data from different modalities during the feature extraction phase, generating a unified cross-modal representation that effectively resolves inter-modal heterogeneity. In conclusion, we posit that multimodal fusion holds significant potential for further enhancing detection accuracy beyond the capabilities of existing single-modality technologies and is poised to become a major focus of future research in this domain. Full article

To ensure the safe and reliable operation of hydrogen-blended natural gas (HBNG) pipelines in urban utility tunnels, this study conducted a comprehensive CFD simulation of the leakage and diffusion characteristics of HBNG in confined underground environments. Utilizing ANSYS CFD software (2024R1), a three-dimensional physical model of a utility tunnel was developed to investigate the influence of key parameters, such as leak sizes (4 mm, 6 mm, and 8 mm)—selected based on common small-orifice defects in utility tunnel pipelines (e.g., corrosion-induced pinholes and minor mechanical damage) and hydrogen blending ratios (HBR) ranging from 0% to 20%—a range aligned with current global HBNG demonstration projects (e.g., China’s “Medium-Term and Long-Term Plan for Hydrogen Energy Industry Development”) and ISO standards prioritizing 20% as a technically feasible upper limit for existing infrastructure, on HBNG diffusion behavior. The study also evaluated the adequacy of current accident ventilation standards. The findings show that as leak orifice size increases, the diffusion range of HBNG expands significantly, with a 31.5% increase in diffusion distance and an 18.5% reduction in alarm time as the orifice diameter grows from 4 mm to 8 mm. Furthermore, hydrogen blending accelerates gas diffusion, with each 5% increase in HBR shortening the alarm time by approximately 1.6 s and increasing equilibrium concentrations by 0.4% vol. The current ventilation standard (12 h⁻¹) was found to be insufficient to suppress concentrations below the 1% safety threshold when the HBR exceeds 5% or the orifice diameter exceeds 4 mm—thresholds derived from simulations showing that, under 12 h⁻¹ ventilation, equilibrium concentrations exceed the 1% safety threshold under these conditions. To address these gaps, this study proposes an adaptive ventilation strategy that uses variable-frequency drives to adjust ventilation rates in real time based on sensor feedback of gas concentrations, ensuring alignment with leakage conditions, thereby ensuring enhanced safety. These results provide crucial theoretical insights for the safe design of HBNG pipelines and ventilation optimization in utility tunnels. Full article

The alarm calls of non-human primates help us to understand the evolution of animal vocal communication and the origin of human language. However, as there is a lack of research on alarm calls in primates living in multilevel societies, we studied these calls in wild Sichuan snub-nosed monkeys. By means of playback experiments, we analyzed whether call receivers understood the meaning of the alarm calls, making appropriate behavioral responses. Results showed that receivers made appropriate and specific anti-predator responses to two types of alarm calls. After hearing the aerial predator alarm call (“GEGEGE”), receivers’ first gaze direction was usually upward (towards the sky), and upward gaze duration was longer than the last gaze before playback. After hearing the terrestrial predator alarm call (“O-GA”), the first gaze direction was usually downward (towards the ground), and this downward gaze duration was longer than the gaze before playback. These reactions provide evidence for external referentiality of alarm calls in Sichuan snub-nosed monkeys, that is, information about the type of predator or the appropriate response is encoded acoustically in the calls. Full article

In the context of intelligent coal mine safety monitoring, an integrated prediction and early-warning method named MTGNN-Bayesian-IF-DBSCAN (Multi-Task Graph Neural Network–Bayesian Optimization–Isolation Forest–Density-Based Spatial Clustering of Applications with Noise) is proposed to address the challenges of gas concentration prediction and anomaly detection in coal mining faces. The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. By constructing a graph structure based on sensor spatial dependencies and utilizing temporal convolutional layers to capture long short-term time-series features, the high-precision dynamic prediction of gas concentrations is achieved via the MTGNN. Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). For anomaly detection, a Bayesian optimization framework is introduced to adaptively optimize the fusion weights of IF (Isolation Forest) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Through defining the objective function as the F1 score and employing Gaussian process surrogate models, the optimal weight combination (w_if = 0.43, w_dbscan = 0.52) is determined, achieving an F1 score of 1.0. By integrating original concentration data and residual features, gas anomalies are effectively identified by the proposed method, with the detection rate reaching a range of 93–96% and the false alarm rate controlled below 5%. Multidimensional analysis diagrams (e.g., residual distribution, 45° diagonal error plot, and boxplots) further validate the model’s robustness in different spatial locations, particularly in capturing abrupt changes and low-concentration anomalies. This study provides a new technical pathway for intelligent gas warning in coal mines, integrating spatiotemporal modeling, multi-algorithm fusion, and statistical optimization. The proposed framework not only enhances the accuracy and reliability of gas prediction and anomaly detection but also demonstrates potential for generalization to other industrial sensor networks. Full article

As the key non-electric protection equipment of an oil-immersed transformer, the gas relay plays an important role in ensuring the safe operation of the transformer. To further enhance the sensitivity of gas relays for the heavy gas alarm, this paper takes the BF type double float gas relay as the research object and proposes a new method for heavy gas setting, which is based on the internal oil flow acceleration characteristics of the gas relay. Firstly, the analytical derivation of the force acting on the gas relay baffle is carried out, and through theoretical analysis, the internal mechanism of heavy gas action under transient oil flow excitation is revealed. Then, the numerical simulation and experimental research on the variation of oil flow velocity and acceleration under different fault energies are carried out. The results show that with the increase of fault energy, the oil flow velocity fluctuates up and down during heavy gas action, but the oil flow acceleration shows a linear correlation. The oil flow acceleration can be set as the threshold of heavy gas action, and the severity of the fault can be judged. At the same time, the alarm time of the heavy gas setting method based on the oil flow acceleration characteristics is greatly shortened, which can reflect the internal fault of the transformer in time and significantly improve the sensitivity of the heavy gas alarm. Full article

Gas turbine (GT) modeling and optimization have been widely studied at the design level but still lacks focus on real-world operational cases. The concept of a digital twin (DT) allows for the interaction between operation data and the system dynamic performance. Among many DT studies, only a few focus on GT for thermal power plants. This study proposes a digital twin prototype framework including the following modules: process modeling, parameter estimation, and performance optimization. Provided with real-world power plant operational data, key performance parameters such as turbine inlet temperature (TIT) and specific fuel consumption (SFC) were initially unavailable, therefore necessitating further calculation using thermodynamic analysis. These parameters are then used as a target label for developing artificial neural networks (ANNs). Three ANN models with different structures are developed to predict TIT, SFC, and turbine power output (GTPO), achieving high R² scores of 94.03%, 82.27%, and 97.59%, respectively. Physics-informed neural networks (PINNs) are then employed to estimate the values of the air–fuel ratio and combustion efficiency for each time index. The PINN-based estimation resulted in estimated values that align with the literature. Subsequently, an unconventional method of detecting alarms by using conformal prediction were also proposed, resulting in a significantly reduced number of alarms. The developed ANNs are then combined with particle swarm optimization (PSO) to carry out performance optimization in real time. GTPO and SFC are selected as the primary metrics for the optimization, with controllable parameters such as AFR and a fine-tuned inlet guide vane position. The results demonstrated that GTPO could be optimized with the application of conformal prediction when the true GTPO is detected to be higher than the upper range of GTPO obtained from the ANN model with a conformal prediction of a 95% confidence level. Multiple PSO variants were also compared and benchmarked to ensure an enhanced performance. The proposed PSO in this study has a lower mean loss compared to GEP. Furthermore, PSO has a lower computational cost compared to RS for hyperparameter tuning, as shown in this study. Ultimately, the proposed methods aim to enhance GT operations via a data-driven digital twin concept combination of deep learning and optimization algorithms. Full article

With the expansion of oil and gas exploration and development to complex oil and gas resource areas such as deep and ultra-deep formation onshore and offshore, the kick is one of the high drilling risks, and timely and accurate early kick detection is increasingly important. Based on the kick generation mechanism, kick characterization parameters are preliminarily selected. According to the characteristics of the data and previous research progress, Random Forest (RF), Support Vector Machine (SVM), Feedforward Neural Network (FNN), and Long Short-term Memory Neural Network (LSTM) are established using experimental data from Memorial University of Newfoundland. The test results show that the accuracy of the SVM-linear model was 0.968, and the missing alarm and the false alarm rate only was 0.06 and 0.11. Additionally, through the analysis of the kick response time, the lag time of the SVM-linear model was 1.3 s, and the comprehensive equivalent time was 23.13 s, which showed the best performance. The different effects of the model after data transformation are analyzed, the mechanism of the best effect of the SVM model is analyzed, and the changes in the effect of other models including RF are further revealed. The proposed early-warning model warns in advance in historical well logging data, which is expected to provide a fast, efficient, and accurate gas kick warning model for drilling sites. Full article

Poisoning and suffocation accidents occurred frequently in the pre-treatment pool workshops of biogas plants, so this paper provided a multi-dimensional risk analysis model: Bow-Tie-Qualitative Comparative Analysis (QCA)-Bayesian Neural Network-Consequence Simulation. First, the reasons for biogas poisoning and suffocation accidents were clarified through Bow-Tie. Then, the QCA method explored the accident cause combination paths in management. Next, the frequency distribution of biogas poisoning and suffocation accidents in the pre-treatment pool workshop was predicted to be 0.61–0.66 using the Bayesian neural network model, and the uncertainty of the forecast outcome was given. Finally, the ANSYS Fluent 16.0 simulation of biogas diffusion in three different ventilation types and a grid-independent solution of the simulation were conducted. The simulation results showed the distribution of methane, carbon dioxide and hydrogen sulfide gases and the hazards of the three gases to workers were analyzed. In addition, according to the results, this paper discussed the importance and necessity of ventilation in pre-treatment pool workshops and specified the hazard factors in biogas poisoning and suffocation accidents in the pre-treatment pool workshops. Some suggestions on gas alarms were also proposed. Full article